If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

CM

A patient and coherent introduction. At the end, you have good working code you can use elsewhere. Remarkably, the primary lecturer, Laurence Moroney, responds fairly quickly to posts in the forum.

RC

May 15, 2019

Filled StarFilled StarFilled StarFilled StarFilled Star

Excellent material superbly presented by world-class experts.\n\nSorry if this sounds sycophantic, but this series contains some of the best courses I've encountered in50+ years of learning.

レッスンから

Exploring a Larger Dataset

In the first course in this specialization, you had an introduction to TensorFlow, and how, with its high level APIs you could do basic image classification, an you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification!
In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification!

講師

Laurence Moroney

AI Advocate

字幕

Before we go, let's have a quick look at plotting the learning history of this model. The object has training accuracy and loss values as well as validation accuracy and validation loss values. So let's iterate over these and plot them. Now, if you look closely, we didn't just call model.fit, we said history equals model.fit. So we now have a history object that we can query for data. Here you can see I'm using the same history object, and I'm calling its history property passing at ACC which gets me the model accuracy. When I run it and plot the training and validation accuracy, we can see that my training went towards one while my validation leveled out into 0.7 to 0.75 range. That shows that my model isn't bad, but I didn't really earn anything after just two epochs. It fits the training data very well with the validation data needed work. These results are borne out in the loss where we can see that after two epochs, my training loss went down nicely, but my validation loss climbed. So as it is, my model is about 75 percent accurate-ish after two epochs, and I don't really need to train any further. Remember also that we just used a subset of the full data. Using the entire dataset would likely yield better results. But before we do that, let's look at a few other options, and we'll do that in the next lesson.